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Record W3105429318 · doi:10.11159/iccste20.323

Application of Collaborative Management NEC and DAB to ImproveCommunication and Integration in Multi-Family Building Projects.

2020· article· en· W3105429318 on OpenAlexvenueno aff
Mario Aragon Gonzales, Jair Mamani Zevallos, Jorge de la Torre y Ramos

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEngineering managementKnowledge managementTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

After the success of the Project for the Pan American Games in Peru, the mechanisms adopted by the organization to achieve the objectives proposed in the Project were made known to society.This research is based on adopting the NEC collaborative methodology mainly in the management of Stakeholders.For this reason, it is proposed to solve the problem of lack of communication and integration of Involved in Multifamily building projects, from the information collected about Collaborative Management NEC is made: (1) Analysis and evaluation of Involved.(2) Characteristics and strategies are proposed to manage Stakeholders.Likewise, (3) an analysis of the Dispute Resolution Board (DAB) is carried out and (4) the application of the ICE meetings tool is proposed to guarantee the correct flow of information in the project.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.238
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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